The Best Ai Startups in Europe

An introduction to artificial intelligence

Artificial intelligence (Ai) is a strategically important sector for Europe and it will continue to be a driver for economic development. In popular culture, it is often misrepresented as being the evil robot army that will become self aware and wipe out humans. But it stands for more than that. In this article, all that nitty gritty stuff will be discussed. This article covers what Ai is all about, the European ecosystem compared to the two Ai superpowers: China and the US, and the European startups to look out for.

What is artificial intelligence?

If you’re a tech nerd and know everything there is to know about Ai, skip to the next section on the landscape in Europe. But for the rest of us dummies, have you ever wondered how Siri works? Or how cars like Tesla are able to drive themselves automatically? Or how Netflix and Spotify are able to recommend you a film or song you might like? That’s all thanks to Ai enabled by machine and deeplearning techniques.

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Machine learning, deep learning and neural networks

On the surface, Ai is a smart device that can self-drive a car, or play you songs you might like. If you peek inside, you’ll find all the intricacies that make Ai possible, that being machine learning and deep learning techniques.

With machine learning, you can teach a computer how to do a particular task by feeding it data. Eventually the machine learning model will process that information and learn from it, growing more accurate with time.

Deep learning on the other hand is different. The two are technically the same, and work in similar ways, but deep learning has a different capacity and is a subset of machine learning. It uses “Artificial Neural Networks” to make decisions without the interference of a human.

It is far more complex and exciting than its counterpart. Instead of training a computer with data sets like you would with machine learning, you give the computer instructions and an expected end point. From those instructions, the computer will find the optimal way to reach that “end point”.

There are lots of different machine learning techniques. And one of the most popular techniques for training a model is through a neural network. A neural network is technically a digital model of the brain that performs specific tasks.

A simplified version of a neural network | Photo from Medium

The more developed a neural network, the more powerful it is, and the better it will become at taking in information and processing it. Just like the human brain, it will retain this information and learn.

Neural networks can be combined to create multiple networks. This means that instead of being able to perform one task at a time, a computer is able to perform multiple tasks at the same time. For example, this is how a Tesla is able to control its speed, detect humans, and steer the wheel all at once. Without deep learning with multiple neural networks this would not be possible.

The European artificial intelligence startups

There are three main factors that are driving innovation in automotive technologies.

The progress of machine learning algorithms

The huge increase in computing capacity

The troves of data that are constantly being produced and used to train machine learning models.

Some 60 years ago, Ai was a theory reserved for academics and test labs. But today it’s become commercial. That being said, it’s far from the point of surpassing human level intelligence. But there’s still some pretty cool startups popping up in Europe. And there are some industries using this technology that are years ahead of the rest.

We’re already interacting on a daily basis with this technology. Voice recognition, self driving cars, search engine precision, data analytics, and most important of all, your Netflix recommendations, are just some examples of the integration and applications of artificial intelligence in our every day lives.

Who are the main players?

The top industries vary in size, with data analytics companies dominating the scene. This, in part, is attributed to the gaps left by large companies like Google Analytics. These startups are filling in the gaps by creating new and innovative analytic companies. Venture capital is also available, more so than for other industries as it has become apparent that data analytics is crucial to many professions. It is helping to streamline the masses of data that can take days to manually aggregate, whereas startups using Ai are able to quicken the process with more accurate results.

Description: Aiva composes emotional soundtracks for films, video games, commercials and any type of entertainment content by the creation of a mathematical model representation of what music is to write unique music.

Ai in the EU

Market potential

Europe is not the leading continent when it comes to Ai, that crown is taken by China and America. This is worrying for the EU as they are the second largest economic power. If they don’t step up their game soon, they’ll fall behind.

That is not to say that they aren’t making advances. Quite the opposite. They’re just unable to translate their digital startups using Ai to the global market, with the exception of a few companies like Spotify. But of the digital companies in Europe, many of them have been acquired by international companies. And Europe does have the potential to become the largest Ai market in terms of size and market value. But there is one crucial factor that is holding them back – cross-border purchasing.

China and America have the advantage – they are not held back by national borders or currency. But Europe is divided borders, with each country having different policy in regards to data, and vast cultural difference. This makes purchasing of digital services between European countries far less common.

And from sector to sector, the adoption is dispersed too. Generally some sectors are further ahead, for example, financial services, media, and tech. Although public sectors, fragmented sectors, and asset-heavy sectors are lagging behind. Mckinsey has estimated that Europe has barely reached its potential in the form of digital technologies. And for the EU to fully tap into their potential there are a few steps to take.

Set targets – Governments need to set realistic targets for public sectors, and therefor set an example for other sectors.

A Digital Single Market – Creating a Single Market for digital companies will make it easier for companies to scale across European borders (introduced in 2015). Steps have already been taken, for example, the abolishment of mobile roaming charges in the EU in July 2017, and GDPR in May 2018.

Investing – Investment in more experimental Ai will help boost the ecosystem. I.e. support for early-stage companies in the form of access to capital and incentives.

Support education – Better relationships between businesses and universities to ensure the continued growth of talent in the EU.

Support grass-root initiatives – R&D differs from nation to nation, and especially between countries. Initiatives like CLAIRE and ELLIS look to solve this issue by seeking European research networks.

It’s an opportunity for companies to seriously enhance their performance capabilities and have a competitive edge. It will majorly increase the productivity within workplaces, improve data analysis, and could provide entirely new solutions and possibilities for companies.

Investment in Ai

Currently, 400+ Ai startups reside in continental Europe, and the number is growing. The United Kingdom remains the dominating force in Europe, with London taking the lead with over 90 startups, followed by Germany (Berlin), France (Paris), and Spain (Madrid). Both the UK and France have made investment in Ai a top priority, and it is hoped that other European countries will follow suit.

The European Commission (EC) has also proposed a legislation to open up data held by public sectors like transport and healthcare. This strategic move will hopefully bolster investment toward the technology. With a growing sector, the EC has noticed the need for support to increase the growth of these early-stage companies. They’ve recently pledged an increase for annual investment in Ai by 70% to 1.5 billion euros under the programme Horizon 2020 (for innovation and research).

This recent investment will hopefully prevent the EU from losing a competitive edge, and avoid a brain drain as some of the top academics in computer science have been educated in Europe, but many have left to jobs abroad. This doesn’t come as a surprise considering that US private investments totalled 18 billion euros, marking the US as the investment leader.

Europe pales in comparison with around 2-3 billion euros. But investments are increasing, and projections for 2020 are looking promising with the EU commission urging public and private investors to reach an expected 20 billion euros.

Ai, is it the end of us?

There are two types of artificial intelligence that are worth mentioning – narrow and general. We’ve created Artificial Narrow Intelligence (ANI). And it’s programmed to handle specific task, and extremely well.

One example of ANI is Alpha Go Zero. This deep learning algorithm was created to play the game Go. Go is the oldest (3,000+ years old) and most studied game in human history, and stands as a great example for how far we’ve come with Ai. Originally, Alpha Go, the first version of this algorithm, was trained with classic machine learning approaches of supervised and reinforced learning using human data sets. In English, that means the algorithm was taught Go by humans.

The second version, Alpha Go Zero, was trained differently. They removed all human interference from the process and instead fed the algorithm the rules of the game. Alpha Go Zero played only against itself, and beat the human taught algorithm in the ancient game 100-0 in just 40 days. It also created entirely new moves unknown to humans. Now, keep in mind, this is only narrow intelligence and it has already surpassed human level intelligence, even if only in one area.

Then there’s Artificial General Intelligence. We’re not there yet. But this hypothetical intelligence would be capable of surpassing human-level intelligence and an ability to think freely and flexibly across different areas. It is this form of Ai that the (let’s call them laggards) are creating a fuss over. This fear of Ai is causing policy makers and the public to shy away from this technology.

Why is this bad? And what are the dangers of artificial intelligence?

Because it is creating a barrier stopping us from adopting this new technology wave. Technology, through history, has proven to substantially increase the productivity of nations, thus creating jobs. But instead, we all to often associate this technology with a malevolent robot that is coming to take our jobs.

Instead, we need to teach our generations to adapt with the new wave, transitioning alongside this promising tech wave smoothly. We’re still in the very early stages of this new technology. It’s incredibly expensive and not quite powerful enough to make a noticeable impact on our economies. But in time it certainly will.

The real threat comes from the misuse of this technology. Take China and America as an example. Warfare funding, monitoring of citizens, and lack of data privacy is just the start. But there a plenty of positives that come with new technology, and it will – and already has to a certain extent – made our lives more convenient and efficient. But there is a dark side to every silver lining. There is complexity to the dangers as it involves so many different sectors. Autonomous weaponry is one extreme example. Social manipulation and privacy is another, does Cambridge Analytica ring any bells, or what about China’s social grading system?

But the EU is taking the necessary steps to ensure that they are paving the way for a responsible and exemplary path, minus of few hiccups along the way. GDPR is first of many policy changes put in place to ensure the safety and correct handling of data.

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Conclusion

Ai is completely transforming the way that we work, and the future of our industries. For industries, machine learning is helping companies to streamline processes in retail, transport, finance and many other sectors. Processes that were otherwise tedious and repetitive for humans are now being replaced by Ai, allowing for us to use our time in the most optimal way.

Maybe Europe will never become a dominant player in Ai, but they could become a driver of change. The current dominating markets are far from perfect. China uses Ai to manage, monitor, and control its people. Whereas the US has little regard for the data privacy of its citizens. And both China and the US are investing significantly in autonomous weaponry. The European Union could take this opportunity to shape their market into a role model for a transparent ecosystem that benefits its citizens.

VALUER.AI

In order to achieve this, Valuer developed a platform that uses crowdsourcing and artificial intelligence to detect, predict, evaluate and select startups that are particularly relevant to large companies.